Ali Karimoddini

A. Karimoddini, H. Lin, B. M. Chen et al.

Teams of Unmanned Aerial Vehicles (UAVs) form typical networked cyber-physical systems that involve the interaction of discrete logic and continuous dynamics. This paper presents a hybrid supervisory control framework for the three-dimensional leader follower formation control of unmanned helicopters. The proposed hybrid control framework captures internal interactions between the decision making unit and the path planner continuous dynamics of the system, and hence improves the system's overall reliability. To design such a hybrid controller, a spherical abstraction of the state space is proposed as a new method of abstraction. Utilizing the properties of multi-affine functions over the partitioned space leads to a finite state Discrete Event System (DES) model, which is shown to be bisimilar to the original continuous-variable dynamical system. Then, in the discrete domain, a logic supervisor is modularly designed for the abstracted model. Due to the bisimilarity between the abstracted DES model and the original UAV dynamics, the designed logic supervisor can be implemented as a hybrid controller through an interface layer. This supervisor drives the UAV dynamics to satisfy the design requirements. In other words, the hybrid controller is able to bring the UAVs to the desired formation starting from any initial state inside the control horizon and then, maintain the formation. Moreover, a collision avoidance mechanism is embedded in the designed supervisor. Finally, the algorithm has been verified by a hardware-in-the-loop simulation platform, which is developed for unmanned helicopters. The presented results show the effectiveness of the algorithm.

Abdollah Homaifar, Ali Karimoddini, Mike Heiges et al.

The North Carolina Agriculture and Technical State University (NC A&T) in collaboration with Georgia Tech Research Institute (GTRI) has developed methodologies for creating simulation-based technology tools that are capable of inferring the perceptions and behavioral states of autonomous systems. These methodologies have the potential to provide the Test and Evaluation (T&E) community at the Department of Defense (DoD) with a greater insight into the internal processes of these systems. The methodologies use only external observations and do not require complete knowledge of the internal processing of and/or any modifications to the system under test. This paper presents an example of one such simulation-based technology tool, named as the Data-Driven Intelligent Prediction Tool (DIPT). DIPT was developed for testing a multi-platform Unmanned Aerial Vehicle (UAV) system capable of conducting collaborative search missions. DIPT's Graphical User Interface (GUI) enables the testers to view the aircraft's current operating state, predicts its current target-detection status, and provides reasoning for exhibiting a particular behavior along with an explanation of assigning a particular task to it.

Muhammad Mobaidul Islam, Abdullah Al Redwan Newaz, Ali Karimoddini

This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. To deal with the challenges associated with the autonomous driving scenarios, an integrated tracking and detection framework is proposed. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates both in RGB and depth images. To provide accurate information, the detection phase is further enhanced by fusing multi-modal sensor information using the Kalman filter. The tracking phase is a combination of the Kalman filter prediction and an optical flow algorithm to track multiple pedestrians in a scene. We evaluate our framework on a real public driving dataset. Experimental results demonstrate that the proposed method achieves significant performance improvement over a baseline method that solely uses image-based pedestrian detection.

Solomon Gudeta, Ali Karimoddini

This paper presents a landing controller for a fixed-wing aircraft during the landing phase, ensuring the aircraft reaches the touchdown point smoothly. The landing problem is converted to a finite-time linear quadratic tracking (LQT) problem in which an aircraft needs to track the desired landing path in the longitudinal-vertical plane while satisfying performance requirements and flight constraints. First, we design a smooth trajectory that meets flight performance requirements and constraints. Then, an optimal controller is designed to minimize the tracking error, while landing the aircraft within the desired time frame. For this purpose, a linearized model of an aircraft developed under the assumption of a small flight path angle and a constant approach speed is used. The resulting Differential Riccati equation is solved backward in time using the Dormand Prince algorithm. Simulation results show a satisfactory tracking performance and the finite-time convergence of tracking errors for different initial conditions of the flare-out phase of landing.